Endophenotype Network-based Approaches to Prediction and Population-based Validation of in Silico Drug Repurposing for Alzheimers Disease

尽管研究人员已经进行了400多项人体试验，以寻找治疗阿尔茨海默氏症的潜在方法， 在过去的二十年中，疾病（AD）的流失率估计超过99%。此外，“一个基因， “一种药物，一种疾病”的简化主义范式忽视了疾病的内在复杂性， 继续挑战AD的药物发现。AD的易感性涉及复杂的、多基因的和 多效性遗传结构最近的研究表明，AD通常具有共同的潜在 与许多其他复杂疾病共享中间内在表型。这些 内源性表型如淀粉样变性和tau蛋白病在许多神经变性疾病中具有重要作用。 系统识别和表征新的潜在发病机制和疾病模块，更重要的是 而不是突变基因，将作为产生可操作目标的基础，作为药物再利用的输入 并合理设计AD的联合治疗方案。基因组、转录组、蛋白质组和 人类相互作用物组对于这种鉴定是必不可少。根据我们的初步结果，我们对该网络进行了优化- 基于新的风险基因和内表型模块的鉴定， 与分别靶向淀粉样蛋白和tau相比，tau为AD的药物治疗提供了意想不到的机会。到 解决基本假设，我们建议建立一个综合的跨学科研究计划， 三个具体目标。目的1将探索淀粉样蛋白和tau蛋白介导的AD内表型模块-我们将测试 使用我们最近开发的贝叶斯框架， 集成多组学数据（即，全基因组关联研究[GWAS]基因座，单细胞测序， 人脑Hi-C数据）和人类相互作用组。Aim 2将能够搜索现有的药物， 使用网络邻近方法的AD联合治疗-我们将强调网络邻近方法的使用。 邻近方法（即，全基因组定位系统网络[GPSnet]）来识别可重复使用的药物 和有效的组合方案。这将通过整合AD内表型模块来实现 研究结果、公共药物靶点数据库、人类相互作用组和大规模患者纵向声明- 电子病历数据（MarketScan数据库中的2亿多名患者）。目标3将评估 大脑渗透和目标网络参与可重复利用的药物-我们将使用人性化的， 体外血脑屏障、切除的脑组织（离体/原位）和转基因AD模型（即，TgF 344-AD大鼠） 实验性地评估大脑渗透和目标网络参与。评估将基于 与AD相关内在表型模块的网络接近度，这些模块与最大化疗效和 尽量减少副作用。该项目的成功完成将提供强大的网络方法， 生物信息学工具，用于预测和基于人群的计算机药物再利用验证。它还将允许 用于识别新的可重复利用的药物和临床相关的组合疗法，以进行AD试验。

Although researchers have conducted more than 400 human trials for potential treatments of Alzheimer’s disease (AD) in the last two decades, the attrition rate is estimated at over 99%. Furthermore, the “one gene, one drug, one disease” reductionism-informed paradigm overlooks the inherent complexity of the disease and continues to challenge drug discovery for AD. The predisposition to AD involves a complex, polygenic, and pleiotropic genetic architecture. Recent studies have suggested that AD often has common underlying mechanisms, sharing intermediate endophenotypes with many other complex diseases. These endophenotypes, such as amyloidosis and tauopathy, have essential roles in many neurodegenerative diseases. Systematic identification and characterization of novel underlying pathogenesis and disease modules, more so than mutated genes, will serve as a foundation for generating actionable targets as input for drug repurposing and rational design of combination therapy in AD. Integration of the genome, transcriptome, proteome, and the human interactome are essential for such identification. Given our preliminary results, we posit that network- based identification of novel risk genes and endophenotype modules that share degree between amyloid and tau offer unexpected opportunities for drug therapy in AD comparing to targeting amyloid and tau separately. To address the underlying hypothesis, we propose to establish an integrated interdisciplinary research plan with three specific aims. Aim 1 will explore amyloid and tau-mediated endophenotype modules for AD -- We will test the network module hypothesis for amyloid and tau using our recently developed Bayesian framework that integrates multi-omics data (i.e., genome-wide association studies [GWAS] loci, single cell sequencing, and human brain Hi-C data) and the human interactome. Aim 2 will be capable of searching existing drugs and combination therapies for AD using network proximity approaches -- We will emphasize the uses of network proximity approaches (i.e., Genome-wide Positioning Systems network [GPSnet]) to identify repurposable drugs and efficacious combination regimens. This will be accomplished by integrating AD endophenotype module findings, public drug-target databases, the human interactome, and the large-scale patient longitudinal Claims- Electronic Medical Record data (over 200 million patients from the MarketScan database). Aim 3 will evaluate brain penetration and target network engagement for repurposable drugs -- We will use the humanized in vitro blood-brain barrier, resected brain tissues (ex vivo/in situ), and transgenic AD models (i.e., TgF344-AD rats) to experimentally evaluate brain penetration and target network engagement. Evaluation will be based upon network proximity to the AD-related endophenotype modules that are relevant to maximizing efficacy and to minimizing side effects. The successful completion of this project will offer powerful network methodologies and bioinformatics tools for prediction and population-based validation of in silico drug repurposing. It will also allow for the identification of novel repurposable drugs and clinically relevant combination therapies toward AD trials.